
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
import seaborn as sns
import re
import plotly.graph_objects as go
import dash

from warnings import simplefilter
from functools import reduce
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import MinMaxScaler, StandardScaler
from math import ceil
from sklearn.cluster import KMeans, DBSCAN, AffinityPropagation
from sklearn.decomposition import PCA
from sklearn.model_selection import train_test_split
from scipy.spatial.distance import cdist, pdist
from sklearn.metrics import silhouette_score
from dash import dash_table
from IPython.core.display import HTML, display


# Versões dos pacotes usados neste jupyter notebook
%reload_ext watermark
%watermark -a "Herikc Brecher e João Hutner" --iversions

Author: Herikc Brecher e João Hutner

plotly    : 5.1.0
pandas    : 1.2.4
numpy     : 1.19.5
dash      : 2.0.0
seaborn   : 0.11.1
re        : 2.2.1
matplotlib: 3.2.0


simplefilter(action='ignore', category=FutureWarning)
%matplotlib inline
sns.set_theme()


# Seed de Aleatoriedade
seed_ = 194
np.random.seed(seed_)


# Carregamento de todos datasets
dtCustomers = pd.read_csv('../data/olist_customers_dataset.csv', encoding = 'utf8', dtype={'customer_zip_code_prefix': str})
dtGeolocation = pd.read_csv('../data/olist_geolocation_dataset.csv', encoding = 'utf8', dtype={'geolocation_zip_code_prefix': str})
dtOrderItems = pd.read_csv('../data/olist_order_items_dataset.csv', encoding = 'utf8')
dtOrderPayments = pd.read_csv('../data/olist_order_payments_dataset.csv', encoding = 'utf8')
dtOrderReviews = pd.read_csv('../data/olist_order_reviews_dataset.csv', encoding = 'utf8')
dtOrders = pd.read_csv('../data/olist_orders_dataset.csv', encoding = 'utf8')
dtProducts = pd.read_csv('../data/olist_products_dataset.csv', encoding = 'utf8')
dtSellers = pd.read_csv('../data/olist_sellers_dataset.csv', encoding = 'utf8')


regioes = {
    'AC': 'Norte',
    'AL': 'Nordeste',
    'AP': 'Norte',
    'AM': 'Norte',
    'BA': 'Nordeste',
    'CE': 'Nordeste',
    'DF': 'CentroOeste',
    'ES': 'Sudeste',
    'GO': 'CentroOeste',
    'MA': 'Nordeste',
    'MT': 'CentroOeste',
    'MS': 'CentroOeste',
    'MG': 'Sudeste',
    'PA': 'Norte',
    'PB': 'Nordeste',
    'PR': 'Sul',
    'PE': 'Nordeste',
    'PI': 'Nordeste',
    'RJ': 'Sudeste',
    'RN': 'Nordeste',
    'RS': 'Sul',
    'RO': 'Norte',
    'RR': 'Norte',
    'SC': 'Sul',
    'SP': 'Sudeste',
    'SE': 'Nordeste',
    'TO': 'Norte'
}


dtCustomers['regiao'] =  dtCustomers['customer_state'].copy()


dtCustomers = dtCustomers.replace({'regiao': regioes})


dtCustomers.head()


dtSellers['regiao'] =  dtSellers['seller_state'].copy()


dtSellers = dtCustomers.replace({'regiao': regioes})


dtSellers.head()


dtGeolocation['regiao'] =  dtGeolocation['geolocation_state'].copy()


dtGeolocation = dtGeolocation.replace({'regiao': regioes})


dtGeolocation.head()


dtCustomers.head()


dtGeolocation.head()


dtOrderItems.head()


dtOrderPayments.head()


dtOrderReviews.head()


dtOrders.head()


dtProducts.head()


dtSellers.head()


listDtMergeOrders = [dtOrderItems[['order_id', 'freight_value']], dtOrderPayments, dtOrderReviews, dtOrders,\
                    dtOrderItems.groupby('order_id').agg({'order_item_id': max}).reset_index()]


dtGeneralOrders = reduce(lambda left, right: pd.merge(left, right, on = 'order_id'), listDtMergeOrders)


dtGeneralOrders = pd.merge(dtGeneralOrders, dtCustomers[['customer_id', 'customer_city', 'customer_state', 'regiao']],\
                           on = 'customer_id')


dtGeneralOrders.head()


# Mantendo somente Ano, Mes e Dia
dtGeneralOrders['order_purchase_timestamp'] = \
    pd.to_datetime(dtGeneralOrders['order_purchase_timestamp'], format = '%Y-%m-%d %H:%M:%S').dt.to_period('d')


# Mantendo somente Ano, Mes e Dia
dtGeneralOrders['order_delivered_customer_date'] = \
    pd.to_datetime(dtGeneralOrders['order_delivered_customer_date'], format = '%Y-%m-%d %H:%M:%S').dt.to_period('d')


# Mantendo somente Ano, Mes e Dia
dtGeneralOrders['order_estimated_delivery_date'] = \
    pd.to_datetime(dtGeneralOrders['order_estimated_delivery_date'], format = '%Y-%m-%d %H:%M:%S').dt.to_period('d')


dtGeneralOrders['diff_delivery_and_estimate'] = dtGeneralOrders['order_estimated_delivery_date'] -\
                                           dtGeneralOrders['order_delivered_customer_date']


dtGeneralOrders['diff_delivery_and_purchase'] = dtGeneralOrders['order_purchase_timestamp'] -\
                                           dtGeneralOrders['order_delivered_customer_date']


dtGeneralOrders['diff_delivery_and_estimate'] = \
                dtGeneralOrders['diff_delivery_and_estimate'].apply(lambda x: re.sub("[^0-9]", "", str(x)))


dtGeneralOrders['diff_delivery_and_purchase'] = \
                dtGeneralOrders['diff_delivery_and_purchase'].apply(lambda x: re.sub("[^0-9]", "", str(x)))


dtGeneralOrders['diff_delivery_and_estimate'] = pd.to_numeric(dtGeneralOrders['diff_delivery_and_estimate'])


dtGeneralOrders['diff_delivery_and_purchase'] = pd.to_numeric(dtGeneralOrders['diff_delivery_and_purchase'])


dtGeneralOrders.head()


dtGeneralOrders = dtGeneralOrders[['order_id', 'freight_value', 'payment_type', 'payment_installments', 'payment_value',\
                                 'review_score', 'order_status', 'order_item_id',\
                                  'diff_delivery_and_estimate', 'diff_delivery_and_purchase',\
                                  'customer_state', 'regiao']]


dtGeneralOrders.head()


dtGeneralOrders = dtGeneralOrders[dtGeneralOrders['order_status'] == 'delivered']


print(dtGeneralOrders.isna().sum())

order_id                         0
freight_value                    0
payment_type                     0
payment_installments             0
payment_value                    0
review_score                     0
order_status                     0
order_item_id                    0
diff_delivery_and_estimate    1674
diff_delivery_and_purchase       8
customer_state                   0
regiao                           0
dtype: int64


# Dropando valores NA
dtGeneralOrders = dtGeneralOrders.dropna()


dtGeneralOrders = dtGeneralOrders.drop('order_status', axis = 1)


dtGeneralOrders.shape

(114054, 11)


dtGeneralOrders.head()


dtGeneralOrders = dtGeneralOrders.rename(columns = {'order_item_id': 'quantity', 'regiao': 'customer_region'})


dtGeneralOrders.head()


dtGeneralOrders.head()


dtGeneralOrdersProcessado = dtGeneralOrders.copy()


# Realizando LabelEncoder
labelencoderPaymentType = LabelEncoder()
labelencoderCustomerState = LabelEncoder()
labelencoderCustomerRegion = LabelEncoder()

dtGeneralOrdersProcessado['payment_type'] = labelencoderPaymentType.fit_transform(dtGeneralOrders['payment_type'])

dtGeneralOrdersProcessado['customer_state'] =\
                labelencoderCustomerState.fit_transform(dtGeneralOrders['customer_state'])

dtGeneralOrdersProcessado['customer_region'] =\
                labelencoderCustomerRegion.fit_transform(dtGeneralOrders['customer_region'])


dtGeneralOrdersProcessado.head()


colunas_quantitativas = ['freight_value', 'payment_value', 'quantity',\
                         'diff_delivery_and_estimate', 'diff_delivery_and_purchase']


def boxplot_individuais(data, columns, width = 15, height = 8):
    fig = plt.figure()
    fig.subplots_adjust(hspace = 0.4, wspace = 0.4)
    fig.set_figheight(8)
    fig.set_figwidth(15)
    
    columns_adjust = ceil(len(columns) / 3)
    
    for i, column in enumerate(columns):
        ax = fig.add_subplot(columns_adjust, 3, i + 1)
        sns.boxplot(x = data[column])
        
    plt.tight_layout()
    plt.show()


def hist_individual(data, columns, width = 10, height = 15):
    fig = plt.figure()
    fig.subplots_adjust(hspace = 0.4, wspace = 0.4)
    fig.set_figheight(10)
    fig.set_figwidth(15)
    
    columns_adjust = ceil(len(columns) / 3)
    
    for i, column in enumerate(columns):
        ax = fig.add_subplot(columns_adjust, 3, i + 1)
        data[column].hist(label = column)
        plt.title(column)
        
    plt.tight_layout()  
    plt.show()


boxplot_individuais(dtGeneralOrdersProcessado, colunas_quantitativas)


hist_individual(dtGeneralOrdersProcessado, colunas_quantitativas)


scaler = StandardScaler()
#scaler = MinMaxScaler()


dtGeneralOrdersNormalizado = dtGeneralOrdersProcessado.copy()


dtGeneralOrdersNormalizado[colunas_quantitativas] = scaler.fit_transform(dtGeneralOrdersProcessado[colunas_quantitativas])


dtGeneralOrdersNormalizado.head()


boxplot_individuais(dtGeneralOrdersNormalizado, colunas_quantitativas)


hist_individual(dtGeneralOrdersNormalizado, colunas_quantitativas)


print(dtGeneralOrdersProcessado[colunas_quantitativas].skew(),\
      '\nSoma:', sum(abs(dtGeneralOrdersProcessado[colunas_quantitativas].skew())))

freight_value                  5.558939
payment_value                 14.448540
quantity                       6.331864
diff_delivery_and_estimate     2.846043
diff_delivery_and_purchase     3.912217
dtype: float64 
Soma: 33.09760296622308


print(dtGeneralOrdersNormalizado[colunas_quantitativas].skew(),\
      '\nSoma:', sum(abs(dtGeneralOrdersNormalizado[colunas_quantitativas].skew())))

freight_value                  5.558939
payment_value                 14.448540
quantity                       6.331864
diff_delivery_and_estimate     2.846043
diff_delivery_and_purchase     3.912217
dtype: float64 
Soma: 33.09760296622308


print(dtGeneralOrdersProcessado[colunas_quantitativas].kurtosis(),\
      '\nSoma:', sum(abs(dtGeneralOrdersProcessado[colunas_quantitativas].kurtosis())))

freight_value                  58.300861
payment_value                 530.034479
quantity                       68.366469
diff_delivery_and_estimate     32.528515
diff_delivery_and_purchase     40.794495
dtype: float64 
Soma: 730.0248192377511


print(dtGeneralOrdersNormalizado[colunas_quantitativas].kurtosis(),\
      '\nSoma:', sum(abs(dtGeneralOrdersNormalizado[colunas_quantitativas].kurtosis())))

freight_value                  58.300861
payment_value                 530.034479
quantity                       68.366469
diff_delivery_and_estimate     32.528515
diff_delivery_and_purchase     40.794495
dtype: float64 
Soma: 730.024819237751


dtGeneralOrdersFinal = dtGeneralOrdersNormalizado.drop('order_id', axis = 1)


dtGeneralOrdersFinal.head()


dtGeneralOrdersFinal[dtGeneralOrdersFinal.columns.difference(colunas_quantitativas)].head()


# Determinando um range de K
k_range = range(1, 12)


# Aplicando o modelo K-Means para cada valor de K
k_means_var = [KMeans(n_clusters = k).fit(dtGeneralOrdersFinal) for k in k_range]


# Ajustando o centróide do cluster para cada modelo
intertia = [X.inertia_ for X in k_means_var]


# Lista de valores de Inertia (Inertia e WCSS são a mesma coisa)
inertia = []

# Loop para testar os valores de K
for n in range(2 , 12):
    modelo = (KMeans(n_clusters = n,
                     init = 'k-means++', 
                     n_init = 10,
                     max_iter = 300,
                     tol = 0.0001,  
                     random_state = seed_, 
                     algorithm = 'elkan'))
    modelo.fit(dtGeneralOrdersFinal)
    inertia.append(modelo.inertia_)

# Plot
plt.figure(1 , figsize = (15 ,6))
plt.plot(np.arange(2 , 12) , inertia , 'o')
plt.plot(np.arange(2 , 12) , inertia , '-' , alpha = 0.5)
plt.xlabel('Número de Clusters') , plt.ylabel('Inertia')
plt.show()


# Lista de valores de Inertia (Inertia e WCSS são a mesma coisa)
inertia = []

# Loop para testar os valores de K
for n in range(2 , 12):
    modelo = (KMeans(n_clusters = n,
                     init = 'k-means++', 
                     n_init = 10,
                     max_iter = 300, 
                     tol = 0.0001,  
                     random_state = seed_))
    modelo.fit(dtGeneralOrdersFinal)
    inertia.append(modelo.inertia_)

# Plot
plt.figure(1 , figsize = (15 ,6))
plt.plot(np.arange(2 , 12) , inertia , 'o')
plt.plot(np.arange(2 , 12) , inertia , '-' , alpha = 0.5)
plt.xlabel('Número de Clusters') , plt.ylabel('Inertia')
plt.savefig('export/curva_elbow.png')
plt.show()


dtGeneralOrdersTemp = dtGeneralOrdersFinal[:57028].copy()


# Determinando um range de K
k_range = range(1, 10)


# Aplicando o modelo K-Means para cada valor de K (esta célula pode levar bastante tempo para ser executada)
k_means_var = [KMeans(n_clusters = k).fit(dtGeneralOrdersTemp) for k in k_range]


# Ajustando o centróide do cluster para cada modelo
centroids = [X.cluster_centers_ for X in k_means_var]


# Calculando a distância euclidiana de cada ponto de dado para o centróide
k_euclid = [cdist(dtGeneralOrdersTemp, cent, 'euclidean') for cent in centroids]
dist = [np.min(ke, axis = 1) for ke in k_euclid]


# Soma dos quadrados das distâncias dentro do cluster
soma_quadrados_intra_cluster = [sum(d**2) for d in dist]


# Soma total dos quadrados
soma_total = sum(pdist(dtGeneralOrdersTemp)**2)/dtGeneralOrdersTemp.shape[0]


# Soma dos quadrados entre clusters
soma_quadrados_inter_cluster = soma_total - soma_quadrados_intra_cluster


# Curva de Elbow
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(k_range, soma_quadrados_inter_cluster/soma_total * 100, 'b*-')
ax.set_ylim((0,100))
plt.grid(True)
plt.xlabel('Número de Clusters')
plt.ylabel('Percentual de Variância Explicada')
plt.title('Variância Explicada x Valor de K')
plt.savefig('export/variancia_explicada.png')
plt.show()


def testa_k(pca_data, k = 2):
    # Criando um modelo
    modelo = KMeans(n_clusters = k)
    modelo.fit(pca_data)
    
    # Obtém os valores mínimos e máximos e organiza o shape
    x_min, x_max = pca_data[:, 0].min() - 5, pca_data[:, 0].max() - 1
    y_min, y_max = pca_data[:, 1].min() + 1, pca_data[:, 1].max() + 5
    xx, yy = np.meshgrid(np.arange(x_min, x_max, .02), np.arange(y_min, y_max, .02))
    Z = modelo.predict(np.c_[xx.ravel(), yy.ravel()])
    Z = Z.reshape(xx.shape)
    
    # Plot das áreas dos clusters
    plt.figure(figsize = (8, 8))
    plt.clf()
    plt.imshow(Z, 
               interpolation = 'nearest',
               extent = (xx.min(), xx.max(), yy.min(), yy.max()),
               cmap = plt.cm.Paired,
               aspect = 'auto', 
               origin = 'lower')
    plt.savefig('export/meshgrid_' + str(k) + 'k.png')
    plt.show()
    
    # Plot dos centróides
    plt.figure(figsize = (13, 13))
    plt.plot(pca_data[:, 0], pca_data[:, 1], 'k.', markersize = 4)
    centroids = modelo.cluster_centers_
    inert = modelo.inertia_
    plt.scatter(centroids[:, 0], centroids[:, 1], marker = 'x', s = 169, linewidths = 5, color = 'r', zorder = 8)
    plt.xlim(x_min, x_max)
    plt.ylim(y_min, y_max)
    plt.xticks(())
    plt.yticks(())
    plt.savefig('export/centroides_' + str(k) + 'k.png')
    plt.show()
    
    # Silhouette Score
    labels = modelo.labels_ 
    print(f"Silhouette Score: {silhouette_score(pca_data, labels, metric = 'euclidean')}")


# Aplica redução de dimensionalidade
pca = PCA(n_components = 2).fit_transform(dtGeneralOrdersFinal)


testa_k(pca, 4)

Silhouette Score: 0.6076782177874883


testa_k(pca, 6)

Silhouette Score: 0.6099490650479382


testa_k(pca, 8)

Silhouette Score: 0.5776603331167324


testa_k(pca, 10)

Silhouette Score: 0.6098466127165733


def treina_modelo(k, data, silhouette = True):
    # Criando um modelo
    modelo = KMeans(n_clusters = k)
    
    modelo.fit(data)
    
    if silhouette:
        # Silhouette Score
        labels = modelo.labels_ 
        print(f"Silhouette Score: {silhouette_score(data, labels, metric = 'euclidean')}")


# Modelo com k = 6
treina_modelo(6, dtGeneralOrdersFinal)

Silhouette Score: 0.3974316315320899


# Modelo com k = 8
treina_modelo(8, dtGeneralOrdersFinal)

Silhouette Score: 0.3184694372738076


# Criando um modelo
modelo = KMeans(n_clusters = 8)
modelo.fit(dtGeneralOrdersFinal)

KMeans()


dtGeneralOrdersClusterizado = dtGeneralOrdersProcessado.copy()


dtGeneralOrdersClusterizado = dtGeneralOrdersClusterizado.drop('order_id', axis = 1)


dtGeneralOrdersClusterizado['cluster'] = modelo.labels_


dtGeneralOrdersClusterizado['payment_type'] =\
                    labelencoderPaymentType.inverse_transform(dtGeneralOrdersClusterizado['payment_type'])


dtGeneralOrdersClusterizado['customer_state'] =\
                    labelencoderCustomerState.inverse_transform(dtGeneralOrdersClusterizado['customer_state'])


dtGeneralOrdersClusterizado['customer_region'] =\
                    labelencoderCustomerRegion.inverse_transform(dtGeneralOrdersClusterizado['customer_region'])


metodos_pagamento = {
    'credit_card': 'Credito',
    'debit_card': 'Debito'
}


dtGeneralOrdersClusterizado = dtGeneralOrdersClusterizado.replace({'payment_type': metodos_pagamento})


dtGeneralOrdersClusterizado.head()


dtClusters = pd.DataFrame()


dtClusters['Preco_Medio'] = round(dtGeneralOrdersClusterizado.groupby('cluster')['payment_value'].mean(), 2)


dtClusters['Frete_Medio'] = round(dtGeneralOrdersClusterizado.groupby('cluster')['freight_value'].mean(), 2)


dtClusters['Media_Pagamentos'] =\
                round(dtGeneralOrdersClusterizado.groupby('cluster')['payment_installments'].mean(), 2)


dtClusters['Score_Medio'] = round(dtGeneralOrdersClusterizado.groupby('cluster')['review_score'].mean(), 2)


dtClusters['Itens_Medio'] = round(dtGeneralOrdersClusterizado.groupby('cluster')['quantity'].mean(), 2)


dtClusters['Dif_Estimado_Entregue'] =\
                round(dtGeneralOrdersClusterizado.groupby('cluster')['diff_delivery_and_estimate'].mean(), 2)


dtClusters['Dif_Compra_Entregue'] =\
                round(dtGeneralOrdersClusterizado.groupby('cluster')['diff_delivery_and_purchase'].mean(), 2)


dtClusters['Pagamento_Prevalente'] =\
                dtGeneralOrdersClusterizado.groupby('cluster')['payment_type'].agg(lambda x: x.value_counts().index[0])


dtClusters['Estado_Prevalente'] =\
                dtGeneralOrdersClusterizado.groupby('cluster')['customer_state'].agg(lambda x: x.value_counts().index[0])


dtClusters['Regiao_Prevalente'] =\
                dtGeneralOrdersClusterizado.groupby('cluster')['customer_region'].agg(lambda x: x.value_counts().index[0])


display(HTML("<style>.container { width:100% !important; }</style>"))


dtClusters


dtClusters.to_csv('analise_clusters.csv')


dtClustersIndexado = dtClusters.reset_index()


dtClustersIndexado = dtClustersIndexado.rename(columns = {'cluster': 'Cluster'})


dtClustersIndexado['Cluster'] = dtClustersIndexado['Cluster'].apply(lambda x: 'Cluster ' + str(x))


headerColor = 'grey'
rowEvenColor = 'lightgrey'
rowOddColor = 'white'

fig = go.Figure(data=[go.Table(
  header=dict(
    values=list(dtClustersIndexado.columns),
    line_color='darkslategray',
    fill_color=headerColor,
    align=['left','center'],
    font=dict(color='white', size=12)
  ),
  cells=dict(
    values=[dtClustersIndexado.Cluster, dtClustersIndexado.Preco_Medio, dtClustersIndexado.Frete_Medio, dtClustersIndexado.Media_Pagamentos,\
       dtClustersIndexado.Score_Medio, dtClustersIndexado.Itens_Medio, dtClustersIndexado.Dif_Estimado_Entregue,\
       dtClustersIndexado.Dif_Compra_Entregue, dtClustersIndexado.Pagamento_Prevalente, dtClustersIndexado.Estado_Prevalente,\
       dtClustersIndexado.Regiao_Prevalente],
    line_color='darkslategray',
    
    fill_color = [[rowOddColor,rowEvenColor,rowOddColor, rowEvenColor]*10],
    align = ['left', 'center'],
    font = dict(color = 'darkslategray', size = 11)
    ))
])

fig.show()

	customer_id	customer_unique_id	customer_zip_code_prefix	customer_city	customer_state	regiao
0	06b8999e2fba1a1fbc88172c00ba8bc7	861eff4711a542e4b93843c6dd7febb0	14409	franca	SP	Sudeste
1	18955e83d337fd6b2def6b18a428ac77	290c77bc529b7ac935b93aa66c333dc3	09790	sao bernardo do campo	SP	Sudeste
2	4e7b3e00288586ebd08712fdd0374a03	060e732b5b29e8181a18229c7b0b2b5e	01151	sao paulo	SP	Sudeste
3	b2b6027bc5c5109e529d4dc6358b12c3	259dac757896d24d7702b9acbbff3f3c	08775	mogi das cruzes	SP	Sudeste
4	4f2d8ab171c80ec8364f7c12e35b23ad	345ecd01c38d18a9036ed96c73b8d066	13056	campinas	SP	Sudeste

	customer_id	customer_unique_id	customer_zip_code_prefix	customer_city	customer_state	regiao
0	06b8999e2fba1a1fbc88172c00ba8bc7	861eff4711a542e4b93843c6dd7febb0	14409	franca	SP	Sudeste
1	18955e83d337fd6b2def6b18a428ac77	290c77bc529b7ac935b93aa66c333dc3	09790	sao bernardo do campo	SP	Sudeste
2	4e7b3e00288586ebd08712fdd0374a03	060e732b5b29e8181a18229c7b0b2b5e	01151	sao paulo	SP	Sudeste
3	b2b6027bc5c5109e529d4dc6358b12c3	259dac757896d24d7702b9acbbff3f3c	08775	mogi das cruzes	SP	Sudeste
4	4f2d8ab171c80ec8364f7c12e35b23ad	345ecd01c38d18a9036ed96c73b8d066	13056	campinas	SP	Sudeste

	geolocation_zip_code_prefix	geolocation_lat	geolocation_lng	geolocation_city	geolocation_state	regiao
0	01037	-23.545621	-46.639292	sao paulo	SP	Sudeste
1	01046	-23.546081	-46.644820	sao paulo	SP	Sudeste
2	01046	-23.546129	-46.642951	sao paulo	SP	Sudeste
3	01041	-23.544392	-46.639499	sao paulo	SP	Sudeste
4	01035	-23.541578	-46.641607	sao paulo	SP	Sudeste

	customer_id	customer_unique_id	customer_zip_code_prefix	customer_city	customer_state	regiao
0	06b8999e2fba1a1fbc88172c00ba8bc7	861eff4711a542e4b93843c6dd7febb0	14409	franca	SP	Sudeste
1	18955e83d337fd6b2def6b18a428ac77	290c77bc529b7ac935b93aa66c333dc3	09790	sao bernardo do campo	SP	Sudeste
2	4e7b3e00288586ebd08712fdd0374a03	060e732b5b29e8181a18229c7b0b2b5e	01151	sao paulo	SP	Sudeste
3	b2b6027bc5c5109e529d4dc6358b12c3	259dac757896d24d7702b9acbbff3f3c	08775	mogi das cruzes	SP	Sudeste
4	4f2d8ab171c80ec8364f7c12e35b23ad	345ecd01c38d18a9036ed96c73b8d066	13056	campinas	SP	Sudeste

	geolocation_zip_code_prefix	geolocation_lat	geolocation_lng	geolocation_city	geolocation_state	regiao
0	01037	-23.545621	-46.639292	sao paulo	SP	Sudeste
1	01046	-23.546081	-46.644820	sao paulo	SP	Sudeste
2	01046	-23.546129	-46.642951	sao paulo	SP	Sudeste
3	01041	-23.544392	-46.639499	sao paulo	SP	Sudeste
4	01035	-23.541578	-46.641607	sao paulo	SP	Sudeste

Seleção da Database¶

Database:¶

Descrição:¶

Problema de Negócio:¶

Observação:¶

1. Carregamento dos Dados¶

2. Pre-Processamento¶

2.1 Feature Engineering¶

2.2 Construindo Datasets¶

2.2.1 Unificando Dataset de Pedidos¶

2.3 Preparando os Dados para Clusterização¶

2.3.1 LabelEncoder¶

2.3.2 Normalização / Padronização¶

2.3.3 Simetria dos Dados¶

2.3.3.1 Skewness¶

2.3.3.2 Kurtosis¶

2.4 Analise de Clusterização¶

2.4.1 Elbow¶

2.4.2 Modelando Clusters KMeans - Com Redução de Componentes¶

2.4.3 Modelando Clusters KMeans - Sem Redução de Componentes¶

2.4.4 Analise de Clusters¶

	order_id	order_item_id	product_id	seller_id	shipping_limit_date	price	freight_value
0	00010242fe8c5a6d1ba2dd792cb16214	1	4244733e06e7ecb4970a6e2683c13e61	48436dade18ac8b2bce089ec2a041202	2017-09-19 09:45:35	58.90	13.29
1	00018f77f2f0320c557190d7a144bdd3	1	e5f2d52b802189ee658865ca93d83a8f	dd7ddc04e1b6c2c614352b383efe2d36	2017-05-03 11:05:13	239.90	19.93
2	000229ec398224ef6ca0657da4fc703e	1	c777355d18b72b67abbeef9df44fd0fd	5b51032eddd242adc84c38acab88f23d	2018-01-18 14:48:30	199.00	17.87
3	00024acbcdf0a6daa1e931b038114c75	1	7634da152a4610f1595efa32f14722fc	9d7a1d34a5052409006425275ba1c2b4	2018-08-15 10:10:18	12.99	12.79
4	00042b26cf59d7ce69dfabb4e55b4fd9	1	ac6c3623068f30de03045865e4e10089	df560393f3a51e74553ab94004ba5c87	2017-02-13 13:57:51	199.90	18.14

	order_id	payment_sequential	payment_type	payment_installments	payment_value
0	b81ef226f3fe1789b1e8b2acac839d17	1	credit_card	8	99.33
1	a9810da82917af2d9aefd1278f1dcfa0	1	credit_card	1	24.39
2	25e8ea4e93396b6fa0d3dd708e76c1bd	1	credit_card	1	65.71
3	ba78997921bbcdc1373bb41e913ab953	1	credit_card	8	107.78
4	42fdf880ba16b47b59251dd489d4441a	1	credit_card	2	128.45

	review_id	order_id	review_score	review_comment_title	review_comment_message	review_creation_date	review_answer_timestamp
0	7bc2406110b926393aa56f80a40eba40	73fc7af87114b39712e6da79b0a377eb	4	NaN	NaN	2018-01-18 00:00:00	2018-01-18 21:46:59
1	80e641a11e56f04c1ad469d5645fdfde	a548910a1c6147796b98fdf73dbeba33	5	NaN	NaN	2018-03-10 00:00:00	2018-03-11 03:05:13
2	228ce5500dc1d8e020d8d1322874b6f0	f9e4b658b201a9f2ecdecbb34bed034b	5	NaN	NaN	2018-02-17 00:00:00	2018-02-18 14:36:24
3	e64fb393e7b32834bb789ff8bb30750e	658677c97b385a9be170737859d3511b	5	NaN	Recebi bem antes do prazo estipulado.	2017-04-21 00:00:00	2017-04-21 22:02:06
4	f7c4243c7fe1938f181bec41a392bdeb	8e6bfb81e283fa7e4f11123a3fb894f1	5	NaN	Parabéns lojas lannister adorei comprar pela I...	2018-03-01 00:00:00	2018-03-02 10:26:53

	order_id	customer_id	order_status	order_purchase_timestamp	order_approved_at	order_delivered_carrier_date	order_delivered_customer_date	order_estimated_delivery_date
0	e481f51cbdc54678b7cc49136f2d6af7	9ef432eb6251297304e76186b10a928d	delivered	2017-10-02 10:56:33	2017-10-02 11:07:15	2017-10-04 19:55:00	2017-10-10 21:25:13	2017-10-18 00:00:00
1	53cdb2fc8bc7dce0b6741e2150273451	b0830fb4747a6c6d20dea0b8c802d7ef	delivered	2018-07-24 20:41:37	2018-07-26 03:24:27	2018-07-26 14:31:00	2018-08-07 15:27:45	2018-08-13 00:00:00
2	47770eb9100c2d0c44946d9cf07ec65d	41ce2a54c0b03bf3443c3d931a367089	delivered	2018-08-08 08:38:49	2018-08-08 08:55:23	2018-08-08 13:50:00	2018-08-17 18:06:29	2018-09-04 00:00:00
3	949d5b44dbf5de918fe9c16f97b45f8a	f88197465ea7920adcdbec7375364d82	delivered	2017-11-18 19:28:06	2017-11-18 19:45:59	2017-11-22 13:39:59	2017-12-02 00:28:42	2017-12-15 00:00:00
4	ad21c59c0840e6cb83a9ceb5573f8159	8ab97904e6daea8866dbdbc4fb7aad2c	delivered	2018-02-13 21:18:39	2018-02-13 22:20:29	2018-02-14 19:46:34	2018-02-16 18:17:02	2018-02-26 00:00:00

	product_id	product_category_name	product_name_lenght	product_description_lenght	product_photos_qty	product_weight_g	product_length_cm	product_height_cm	product_width_cm
0	1e9e8ef04dbcff4541ed26657ea517e5	perfumaria	40.0	287.0	1.0	225.0	16.0	10.0	14.0
1	3aa071139cb16b67ca9e5dea641aaa2f	artes	44.0	276.0	1.0	1000.0	30.0	18.0	20.0
2	96bd76ec8810374ed1b65e291975717f	esporte_lazer	46.0	250.0	1.0	154.0	18.0	9.0	15.0
3	cef67bcfe19066a932b7673e239eb23d	bebes	27.0	261.0	1.0	371.0	26.0	4.0	26.0
4	9dc1a7de274444849c219cff195d0b71	utilidades_domesticas	37.0	402.0	4.0	625.0	20.0	17.0	13.0

	order_id	freight_value	payment_type	payment_installments	payment_value	review_score	quantity	diff_delivery_and_estimate	diff_delivery_and_purchase	customer_state	customer_region
0	00010242fe8c5a6d1ba2dd792cb16214	13.29	1	2	72.19	5	1	9.0	7.0	18	3
1	00018f77f2f0320c557190d7a144bdd3	19.93	1	3	259.83	4	1	3.0	16.0	25	3
2	000229ec398224ef6ca0657da4fc703e	17.87	1	5	216.87	5	1	14.0	8.0	10	3
3	00024acbcdf0a6daa1e931b038114c75	12.79	1	2	25.78	4	1	6.0	6.0	25	3
4	00042b26cf59d7ce69dfabb4e55b4fd9	18.14	1	3	218.04	5	1	16.0	25.0	25	3

	order_id	freight_value	payment_type	payment_installments	payment_value	review_score	quantity	diff_delivery_and_estimate	diff_delivery_and_purchase	customer_state	customer_region
0	00010242fe8c5a6d1ba2dd792cb16214	-0.426745	1	2	-0.375092	5	-0.354355	-0.564103	-0.567311	18	3
1	00018f77f2f0320c557190d7a144bdd3	-0.005297	1	3	0.329257	4	-0.354355	-1.298337	0.385421	25	3
2	000229ec398224ef6ca0657da4fc703e	-0.136047	1	5	0.167997	5	-0.354355	0.047760	-0.461452	10	3
3	00024acbcdf0a6daa1e931b038114c75	-0.458481	1	2	-0.549303	4	-0.354355	-0.931220	-0.673170	25	3
4	00042b26cf59d7ce69dfabb4e55b4fd9	-0.118910	1	3	0.172389	5	-0.354355	0.292504	1.338153	25	3

	freight_value	payment_type	payment_installments	payment_value	review_score	quantity	diff_delivery_and_estimate	diff_delivery_and_purchase	customer_state	customer_region	cluster
0	13.29	Credito	2	72.19	5	1	9.0	7.0	RJ	Sudeste	2
1	19.93	Credito	3	259.83	4	1	3.0	16.0	SP	Sudeste	5
2	17.87	Credito	5	216.87	5	1	14.0	8.0	MG	Sudeste	6
3	12.79	Credito	2	25.78	4	1	6.0	6.0	SP	Sudeste	0
4	18.14	Credito	3	218.04	5	1	16.0	25.0	SP	Sudeste	5

	Preco_Medio	Frete_Medio	Media_Pagamentos	Score_Medio	Itens_Medio	Dif_Estimado_Entregue	Dif_Compra_Entregue	Pagamento_Prevalente	Estado_Prevalente	Regiao_Prevalente
cluster
0	121.76	15.29	1.18	4.18	1.37	12.27	9.67	Credito	SP	Sudeste
1	154.86	23.89	2.02	3.96	1.29	13.90	17.23	Credito	BA	Nordeste
2	143.05	21.42	1.69	3.97	1.32	15.10	14.77	Credito	RJ	Sudeste
3	342.22	22.54	9.02	4.00	1.74	13.59	10.72	Credito	SP	Sudeste
4	318.29	28.49	7.58	3.87	1.52	15.64	15.49	Credito	RJ	Sudeste
5	167.62	17.30	4.10	4.12	1.40	13.04	10.01	Credito	SP	Sudeste
6	152.00	22.12	1.93	4.08	1.34	14.28	13.64	Credito	MG	Sudeste
7	347.74	29.81	8.30	3.94	1.66	14.91	14.86	Credito	MG	Sudeste